def make_matrices():
# Find all the relevant files
#allfiles = [f for f in os.listdir("./") if f.startswith("RV") and f.endswith("-0.fits")]
allfiles = [f for f in os.listdir("./") if f.startswith("RV") and '-0' in f and f.endswith("corrected.fits")]
# Read in the measured RV data
bjd, rv = np.loadtxt("psi1draa_100_120_mcomb1.dat", usecols=(0,1), unpack=True)
bjd2, vbary_arr = np.loadtxt("psi1draa_100p_28_37_ASW.dat", usecols=(0,5), unpack=True)
# Put all the data in one giant list
alldata = []
print "Reading all data"
for i, fname in enumerate(allfiles):
header = fits.getheader(fname)
jd = header['jd']
#vbary = GenericSearch.HelCorr(header, observatory="McDonald")
idx = np.argmin(abs(bjd2 - jd))
bjd2_i = bjd2[idx]
vbary = vbary_arr[idx] / 1000.0
idx = np.argmin(abs(bjd - jd))
bjd_i = bjd[idx]
vstar = rv[idx]/1e3
print(fname, vbary, vstar)
vel = vbary - vstar #Closest... but still not great
#vel = -vbary + vstar #NO
#vel = vbary + vstar #NO
#vel = -vbary - vstar #NO
#vel = vstar #NO
#vel = -vstar #NO
#vel = vbary #Surprisingly not bad...
orders = HelperFunctions.ReadExtensionFits(fname)[:-2]
for j in badorders[::-1]:
orders.pop(j)
if i == 0:
template = []
for j, order in enumerate(orders):
order.x *= (1.0+vel/c)
orders[j] = order.copy()
template.append(order.copy())
alldata.append([order])
#template = [o.copy() for o in orders]
else:
vel = fit_rv_shift_old(template, orders)
print('RV adjustment = {}'.format(vel))
for j, order in enumerate(orders):
order.x *= (1.0+vel/c)
orders[j] = order.copy()
if i == 0:
alldata.append([order])
#template.append(order.copy())
else:
alldata[j].append(order)
#plt.plot(order.x, order.y/order.cont, 'g-', alpha=0.5)
#plt.show()
# Interpolate each order to a single wavelength grid
print "Interpolating to wavelength grid"
xgrids = []
c_cycler = itertools.cycle(('r', 'g', 'b', 'k'))
ls_cycler = itertools.cycle(('-', '--', ':', '-.'))
for i, order in enumerate(alldata):
print "Order ", i+1
firstwaves = [d.x[0] for d in order]
lastwaves = [d.x[-1] for d in order]
size = np.mean([d.size() for d in order])
xgrid = np.linspace(max(firstwaves), min(lastwaves), size)
for j, data in enumerate(order):
tmp = FittingUtilities.RebinData(data, xgrid)
order[j] = tmp.y/tmp.cont
#if i == 20:
# col = c_cycler.next()
# if j%4 == 0:
# ls = ls_cycler.next()
# plt.plot(xgrid, order[j], color=col, ls=ls, alpha=0.8, label=allfiles[j])
#plt.plot(xgrid, order[j], 'k-', alpha=0.2)
alldata[i] = np.array(order)
xgrids.append(xgrid)
#plt.legend(loc='best')
#plt.show()
return allfiles, xgrids, alldata
fitter.const_pars[j] for j in range(len(fitter.parnames))
if fitter.fitting[j]
]
full_model = fitter.GenerateModel(fitpars,
separate_source=False,
return_resolution=False,
broaden=False,
nofit=True)
for i, order in enumerate(orders):
left = np.searchsorted(full_model.x, order.x[0] - 5)
right = np.searchsorted(full_model.x, order.x[-1] + 5)
if min(full_model.y[left:right]) > 0.95:
model = FittingUtilities.ReduceResolution(
full_model[left:right].copy(), resolution)
model = FittingUtilities.RebinData(model, order.x)
data = order.copy()
data.cont = np.ones(data.size())
else:
print "\n\nGenerating model for order %i of %i\n" % (
i, len(orders))
order.cont = FittingUtilities.Continuum(order.x,
order.y,
fitorder=3,
lowreject=1.5,
highreject=10)
fitter.ImportData(order)
fitter.resolution_fit_mode = "gauss"
model = fitter.GenerateModel(
fitpars,
def MakeModel(self,
pressure=795.0,
temperature=283.0,
lowfreq=4000,
highfreq=4600,
angle=45.0,
humidity=50.0,
co2=368.5,
o3=3.9e-2,
n2o=0.32,
co=0.14,
ch4=1.8,
o2=2.1e5,
no=1.1e-19,
so2=1e-4,
no2=1e-4,
nh3=1e-4,
hno3=5.6e-4,
lat=30.6,
alt=2.1,
wavegrid=None,
resolution=None,
save=False,
libfile=None,
vac2air=True):
"""
Here is the important function! All of the variables have default values,
which you will want to override for any realistic use.
:param pressure: Pressure at telescope altitude (hPa)
:param temperature: Temperature at telescope altitude (Kelvin)
:param lowfreq: The starting wavenumber (cm^-1)
:param highfreq: The ending wavenumber (cm^-1)
:param angle: The zenith distance of the telescope (degrees). This is related to
the airmass (z) through z = sec(angle)
:param humidity: Percent relative humidity at the telescope altitude.
:param co2: Mixing ratio of this molecule (parts per million by volumne)
:param o3: Mixing ratio of this molecule (parts per million by volumne)
:param n2o: Mixing ratio of this molecule (parts per million by volumne)
:param co: Mixing ratio of this molecule (parts per million by volumne)
:param ch4: Mixing ratio of this molecule (parts per million by volumne)
:param o2: Mixing ratio of this molecule (parts per million by volumne)
:param no: Mixing ratio of this molecule (parts per million by volumne)
:param so2: Mixing ratio of this molecule (parts per million by volumne)
:param no2: Mixing ratio of this molecule (parts per million by volumne)
:param nh3: Mixing ratio of this molecule (parts per million by volumne)
:param hno3: Mixing ratio of this molecule (parts per million by volumne)
:param lat: The latitude of the observatory (degrees)
:param alt: The altitude of the observatory above sea level (km)
:param wavegrid: If given, the model will be resampled to this grid.
Should be a 1D np array
:param resolution: If given, it will reduce the resolution by convolving
with a gaussian of appropriate width. Should be a float
with R=lam/dlam
:param save: If true, the generated model is saved. The filename will be
printed to the screen.
:param libfile: Useful if generating a telluric library. The filename of the
saved file will be written to this filename. Should be a string
variable. Ignored if save==False
:param vac2air: If True (default), it converts the wavelengths from vacuum to air
:return: DataStructures.xypoint instance with the telluric model. The x-axis
is in nanometers and the y-axis is in fractional transmission.
"""
self.FindWorkingDirectory()
#Make the class variables local
TelluricModelingDir = self.TelluricModelingDir
debug = self.debug
lock = self.lock
layers = np.array(self.layers)
ModelDir = self.ModelDir
#Make a deep copy of atmosphere so that I don't repeatedly modify it
Atmosphere = copy.deepcopy(self.Atmosphere)
#Convert from relative humidity to concentration (ppm)
h2o = humidity_to_ppmv(humidity, temperature, pressure)
#Start by scaling the abundances from those at 'alt' km
# (linearly interpolate)
keys = sorted(Atmosphere.keys())
lower = max(0, np.searchsorted(keys, alt) - 1)
upper = min(lower + 1, len(keys) - 1)
if lower == upper:
raise ZeroDivisionError(
"Observatory altitude of %g results in the surrounding layers being the same!"
% alt)
scale_values = list(Atmosphere[lower])
scale_values[2] = list(Atmosphere[lower][2])
scale_values[0] = (Atmosphere[upper][0] - Atmosphere[lower][0]) / (
keys[upper] - keys[lower]) * (alt -
keys[lower]) + Atmosphere[lower][0]
scale_values[1] = (Atmosphere[upper][1] - Atmosphere[lower][1]) / (
keys[upper] - keys[lower]) * (alt -
keys[lower]) + Atmosphere[lower][1]
for mol in range(len(scale_values[2])):
scale_values[2][mol] = (
Atmosphere[upper][2][mol] -
Atmosphere[lower][2][mol]) / (keys[upper] - keys[lower]) * (
alt - keys[lower]) + Atmosphere[lower][2][mol]
#Do the actual scaling
pressure_scalefactor = (scale_values[0] - pressure) * np.exp(
-(layers - alt)**2 / (2.0 * 10.0**2))
temperature_scalefactor = (scale_values[1] - temperature) * np.exp(
-(layers - alt)**2 / (2.0 * 10.0**2))
for i, layer in enumerate(layers):
# Atmosphere[layer][0] -= pressure_scalefactor[i]
Atmosphere[layer][0] *= pressure / scale_values[0]
Atmosphere[layer][1] -= temperature_scalefactor[i]
Atmosphere[layer][2][0] *= h2o / scale_values[2][0]
Atmosphere[layer][2][1] *= co2 / scale_values[2][1]
Atmosphere[layer][2][2] *= o3 / scale_values[2][2]
Atmosphere[layer][2][3] *= n2o / scale_values[2][3]
Atmosphere[layer][2][4] *= co / scale_values[2][4]
Atmosphere[layer][2][5] *= ch4 / scale_values[2][5]
Atmosphere[layer][2][6] *= o2 / scale_values[2][6]
Atmosphere[layer][2][7] *= no / scale_values[2][7]
Atmosphere[layer][2][8] *= so2 / scale_values[2][8]
Atmosphere[layer][2][9] *= no2 / scale_values[2][9]
Atmosphere[layer][2][10] *= nh3 / scale_values[2][10]
Atmosphere[layer][2][11] *= hno3 / scale_values[2][11]
#Now, Read in the ParameterFile and edit the necessary parameters
parameters = MakeTape5.ReadParFile(parameterfile=TelluricModelingDir +
"ParameterFile")
parameters[48] = "%.1f" % lat
parameters[49] = "%.1f" % alt
parameters[51] = "%.5f" % angle
parameters[17] = lowfreq
freq, transmission = np.array([]), np.array([])
#Need to run lblrtm several times if the wavelength range is too large.
maxdiff = 1999.9
if (highfreq - lowfreq > maxdiff):
while lowfreq + maxdiff <= highfreq:
parameters[18] = lowfreq + maxdiff
MakeTape5.WriteTape5(parameters,
output=TelluricModelingDir + "TAPE5",
atmosphere=Atmosphere)
#Run lblrtm
cmd = "cd " + TelluricModelingDir + ";sh runlblrtm_v3.sh"
try:
if self.print_lblrtm_output:
command = subprocess.check_call(cmd, shell=True)
if not self.print_lblrtm_output:
command = subprocess.check_call(
cmd,
shell=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
except subprocess.CalledProcessError:
raise subprocess.CalledProcessError(
"Error: Command '{}' failed in directory {}".format(
cmd, TelluricModelingDir))
#Read in TAPE12, which is the output of LBLRTM
freq, transmission = self.ReadTAPE12(TelluricModelingDir,
appendto=(freq,
transmission))
lowfreq = lowfreq + 2000.00001
parameters[17] = lowfreq
parameters[18] = highfreq
MakeTape5.WriteTape5(parameters,
output=TelluricModelingDir + "TAPE5",
atmosphere=Atmosphere)
#Run lblrtm for the last time
cmd = "cd " + TelluricModelingDir + ";sh runlblrtm_v3.sh"
try:
if self.print_lblrtm_output:
command = subprocess.check_call(cmd, shell=True)
if not self.print_lblrtm_output:
command = subprocess.check_call(cmd,
shell=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL)
except subprocess.CalledProcessError:
raise subprocess.CalledProcessError(
"Error: Command '{}' failed in directory {}".format(
cmd, TelluricModelingDir))
#Read in TAPE12, which is the output of LBLRTM
freq, transmission = self.ReadTAPE12(TelluricModelingDir,
appendto=(freq, transmission))
#Convert from frequency to wavelength units
wavelength = units.cm.to(units.nm) / freq
#Correct for index of refraction of air (use IAU standard conversion from
# Morton, D. C. 1991, ApJS, 77, 119
if vac2air:
wave_A = wavelength * units.nm.to(
units.angstrom) # Wavelength in angstroms
n = 1.0 + 2.735182e-4 + 131.4182 / wave_A**2 + 2.76249e8 / wave_A**4
wavelength /= n
if save:
#Output filename
model_name = ModelDir + "transmission" + "-%.2f" % pressure + "-%.2f" % temperature + "-%.1f" % humidity + "-%.1f" % angle + "-%.2f" % (
co2) + "-%.2f" % (o3 * 100) + "-%.2f" % ch4 + "-%.2f" % (co *
10)
logging.info(
"All done! Output Transmission spectrum is located in the file below:\n\t\t{}"
.format(model_name))
np.savetxt(model_name,
np.transpose((wavelength[::-1], transmission[::-1])),
fmt="%.8g")
if libfile != None:
infile = open(libfile, "a")
infile.write(model_name + "\n")
infile.close()
self.Cleanup() #Un-lock the working directory
if wavegrid != None:
model = DataStructures.xypoint(x=wavelength[::-1],
y=transmission[::-1])
return FittingUtilities.RebinData(model, wavegrid)
return DataStructures.xypoint(x=wavelength[::-1], y=transmission[::-1])
